920 research outputs found
A review on robot motion planning approaches
The ability of a robot to plan its own motion seems pivotal to its autonomy, and that is why the motion planning has become part and parcel of modern intelligent robotics. In this paper, about 100 research are reviewed and briefly described to identify and classify the amount of the existing work for each motion planning approach. Meanwhile, around 200 research were used to determine the percentage of the application of each approach. The paper includes comparative tables and charts showing the application frequency of each approach in the last 30 years. Finally, some open areas and challenging topics are presented based on the reviewed papers
Mobile Robot Navigation in Static and Dynamic Environments using Various Soft Computing Techniques
The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. The present research focuses on the design and implementation of the intelligent navigation algorithms, which is capable of navigating a mobile robot autonomously in static as well as dynamic environments. Navigation and obstacle avoidance are one of the most important tasks for any mobile robots. The primary objective of this research work is to improve the navigation accuracy and efficiency of the mobile robot using various soft computing techniques. In this research work, Hybrid Fuzzy (H-Fuzzy) architecture, Cascade Neuro-Fuzzy (CN-Fuzzy) architecture, Fuzzy-Simulated Annealing (Fuzzy-SA) algorithm, Wind Driven Optimization (WDO) algorithm, and Fuzzy-Wind Driven Optimization (Fuzzy-WDO) algorithm have been designed and implemented to solve the navigation problems of a mobile robot in different static and dynamic environments. The performances of these proposed techniques are demonstrated through computer simulations using MATLAB software and implemented in real time by using experimental mobile robots. Furthermore, the performances of Wind Driven Optimization algorithm and Fuzzy-Wind Driven Optimization algorithm are found to be most efficient (in terms of path length and navigation time) as compared to rest of the techniques, which verifies the effectiveness and efficiency of these newly built techniques for mobile robot navigation. The results obtained from the proposed techniques are compared with other developed techniques such as Fuzzy Logics, Genetic algorithm (GA), Neural Network, and Particle Swarm Optimization (PSO) algorithm, etc. to prove the authenticity of the proposed developed techniques
An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application
With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper
Architecture for planning and execution of missions with fleets of unmanned vehicles
Esta tesis presenta contribuciones en el campo de la planificación automática y la
programación de tareas, la rama de la inteligencia artificial que se ocupa de la
realización de estrategias o secuencias de acciones típicamente para su ejecución por
parte de vehículos no tripulados, robots autónomos y/o agentes inteligentes. Cuando se
intenta alcanzar un objetivo determinado, la cooperación puede ser un aspecto clave. La
complejidad de algunas tareas requiere la cooperación entre varios agentes. Mas aún,
incluso si una tarea es lo suficientemente simple para ser llevada a cabo por un único
agente, puede usarse la cooperación para reducir el coste total de la misma. Para realizar
tareas complejas que requieren interacción física con el mundo real, los vehículos no
tripulados pueden ser usados como agentes. En los últimos años se han creado y utilizado
una gran diversidad de plataformas no tripuladas, principalmente vehículos que pueden
ser dirigidos sin un humano a bordo, tanto en misiones civiles como militares.
En esta tesis se aborda la aplicación de planificación simbólica de redes jerárquicas
de tareas (HTN planning, por sus siglas en inglés) en la resolución de problemas de
enrutamiento de vehículos (VRP, por sus siglas en inglés) [18], en dominios que implican
múltiples vehículos no tripulados de capacidades heterogéneas que deben cooperar para
alcanzar una serie de objetivos específicos.
La planificación con redes jerárquicas de tareas describe dominios utilizando una
descripción que descompone conjuntos de tareas en subconjuntos más pequeños de
subtareas gradualmente, hasta obtener tareas del más bajo nivel que no pueden ser
descompuestas y se consideran directamente ejecutables. Esta jerarquía es similar al modo
en que los humanos razonan sobre los problemas, descomponiéndolos en subproblemas
según el contexto, y por lo tanto suelen ser fáciles de comprender y diseñar.
Los problemas de enrutamiento de vehículos son una generalización del problema del
viajante (TSP, por sus siglas en inglés). La resolución del problema del viajante consiste
en encontrar la ruta más corta posible que permite visitar una lista de ciudades, partiendo
y acabando en la misma ciudad. Su generalización, el problema de enrutamiento de
vehículos, consiste en encontrar el conjunto de rutas de longitud mínima que permite
cubrir todas las ciudades con un determinado número de vehículos. Ambos problemas
cuentan con una fuerte componente combinatoria para su resolución, especialmente en el caso del VRP, por lo que su presencia en dominios que van a ser tratados con un planificador
HTN clásico supone un gran reto.
Para la aplicación de un planificador HTN en la resolución de problemas de enrutamiento
de vehículos desarrollamos dos métodos. En el primero de ellos presentamos un sistema
de optimización de soluciones basado en puntuaciones, que nos permite una nueva forma
de conexión entre un software especializado en la resolución del VRP con el planificador
HTN. Llamamos a este modo de conexión el método desacoplado, puesto que resolvemos
la componente combinatoria del problema de enrutamiento de vehículos mediante un
solucionador específico que se comunica con el planificador HTN y le suministra la
información necesaria para continuar con la descomposición de tareas. El segundo método
consiste en mejorar el planificador HTN utilizado para que sea capaz de resolver el
problema de enrutamiento de vehículos de la mejor forma posible sin tener que depender
de módulos de software externos. Llamamos a este modo el método acoplado. Con
este motivo hemos desarrollado un nuevo planificador HTN que utiliza un algoritmo de
búsqueda distinto del que se utiliza normalmente en planificadores de este tipo.
Esta tesis presenta nuevas contribuciones en el campo de la planificación con redes
jerárquicas de tareas para la resolución de problemas de enrutamiento de vehículos. Se
aplica una nueva forma de conexión entre dos planificadores independientes basada en
un sistema de cálculo de puntuaciones que les permite colaborar en la optimización de
soluciones, y se presenta un nuevo planificador HTN con un algoritmo de búsqueda distinto
al comúnmente utilizado. Se muestra la aplicación de estos dos métodos en misiones
civiles dentro del entorno de los Proyectos ARCAS y AEROARMS financiados por la
Comisión Europea y se presentan extensos resultados de simulación para comprobar la
validez de los dos métodos propuestos.This thesis presents contributions in the field of automated planning and scheduling,
the branch of artificial intelligence that concerns the realization of strategies or
action sequences typically for execution by unmanned vehicles, autonomous robots and/or
intelligent agents. When trying to achieve certain goal, cooperation may be a key aspect.
The complexity of some tasks requires the cooperation among several agents. Moreover,
even if the task is simple enough to be carried out by a single agent, cooperation can be
used to decrease the overall cost of the operation. To perform complex tasks that require
physical interaction with the real world, unmanned vehicles can be used as agents. In the
last years a great variety of unmanned platforms, mainly vehicles that can be driven without
a human on board, have been developed and used both in civil and military missions.
This thesis deals with the application of Hierarchical Task Network (HTN) planning
in the resolution of vehicle routing problems (VRP) [18] in domains involving multiple
heterogeneous unmanned vehicles that must cooperate to achieve specific goals.
HTN planning describes problem domains using a description that decomposes set of
tasks into subsets of smaller tasks and so on, obtaining low-level tasks that cannot be
further decomposed and are supposed to be executable. The hierarchy resembles the way
the humans reason about problems by decomposing them into sub-problems depending
on the context and therefore tend to be easy to understand and design.
Vehicle routing problems are a generalization of the travelling salesman problem (TSP).
The TSP consists on finding the shortest path that connects all the cities from a list, starting
and ending on the same city. The VRP consists on finding the set of minimal routes that
cover all cities by using a specific number of vehicles. Both problems have a combinatorial
nature, specially the VRP, that makes it very difficult to use a HTN planner in domains
where these problems are present.
Two approaches to use a HTN planner in domains involving the VRP have been tested.
The first approach consists on a score-based optimization system that allows us to apply a
new way of connecting a software specialized in the resolution of the VRP with the HTN
planner. We call this the decoupled approach, as we tackle the combinatorial nature of the
VRP by using a specialized solver that communicates with the HTN planner and provides
all the required information to do the task decomposition. The second approach consists on improving and enhancing the HTN planner to be capable of solving the VRP without
needing the use of an external software. We call this the coupled approach. For this reason,
a new HTN planner that uses a different search algorithm from these commonly used in
that type of planners has been developed and is presented in this work.
This thesis presents new contributions in the field of hierarchical task network planning
for the resolution of vehicle routing problem domains. A new way of connecting two
independent planning systems based on a score calculation system that lets them cooperate
in the optimization of the solutions is applied, and a new HTN planner that uses a different
search algorithm from that usually used in other HTN planners is presented. These two
methods are applied in civil missions in the framework of the ARCAS and AEROARMS
Projects funded by the European Commission. Extensive simulation results are presented
to test the validity of the two approaches
On the use of autonomous unmanned vehicles in response to hazardous atmospheric release incidents
Recent events have induced a surge of interest in the methods of response to releases of hazardous materials or gases into the atmosphere. In the last decade there has been particular interest in mapping and quantifying emissions for regulatory purposes, emergency response, and environmental monitoring. Examples include: responding to events such as gas leaks, nuclear accidents or chemical, biological or radiological (CBR) accidents or attacks, and even exploring sources of methane emissions on the planet Mars. This thesis presents a review of the potential responses to hazardous releases, which includes source localisation, boundary tracking, mapping and source term estimation. [Continues.]</div
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Motion control of unmanned ground vehicle using artificial intelligence
The aim of this thesis is to solve two problems: the. trajectory tracking and navigation, for controlling the motion of unmanned ground vehicles (UGV). Such vehicles are usually used in industry for assisting automated production process or delivery services to improve and enhance the quality and efficiency.
With regard to the trajectory tracking problem, the main task is to design a new method that is capable of minimising trajectory-tracking errors in UGV. To achieve this, a comprehensive mathematical model needs to be established that contains kinematic and dynamic characteristics beside actuators. In addition, different trajectories need to be generated and applied individually as a reference input, i.e. continuous gradient trajectories such as linear, circular and lemniscuses or a non-continuous gradient trajectory such as a square trajectory. The design method is based on a novel fractional order proportional integral derivative (FOPID) control strategy, which is proposed to control the movement of UGV to track given trajectories. Two FOPID controllers are required in this design. The first FOPID is constructed in order to control the orientation of UGV. The second FOPID controller is to control the speed of UGV. The particle swarm optimization (PSO) algorithm is used to obtain the optimal parameters for both controllers. The significance of the proposed method is that an observable improvement has been achieved in terms of minimising trajectory-tracking errors and reducing control efforts, especially in continuous gradient trajectories. The stability of the proposed controllers is investigated based upon Nyquist stability criterion. Moreover, the robustness of the controllers is examined in the presence of disturbances to demonstrate the effectiveness of the controllers under certain harsh conditions. The influence from external disturbances has been represented by square pulses and sinusoidal waves. The drawback of this method, however, a highly trajectory tracking error is observed in non-continuous gradient trajectories due to the sharpness of the rotation at the corners of a square trajectory.
To overcome this drawback, a new controller, abbreviated as (NN-FOPID), has been proposed based on a combination of neural networks and the FOPID. The purpose is to minimise the trajectory tracking error of non-continuous trajectories, in particular. The Levenberg-Marquardt (LM) algorithm is used to train the NN-FOPID controller. The neural networks’ cognitive capacities have made the system adaptable to respond effectively to the variants in trajectories. The obtained results by using NN-FOPID have shown a significant improvement of reducing errors of trajectory tracking and increasing control efforts over the results by FOPID.
The other task is to solve the navigation problem of UGV in static and dynamic environments. This can be conducted by firstly constructing workspace environments that contain multiple dynamic and static obstacles. The dynamic obstructing obstacles can move in different velocities. The static obstacles can be randomly positioned in the workspace and all obstacles are allowed to have different sizes and shapes. Secondly, a UGV can be placed in any initial posture on the condition that it has to reach a given destination within the boundaries of the workspace. Thirdly, a method based on fuzzy inference systems (FIS) is proposed to control the motion of the UGV. The design of FIS is based on fuzzification, inference engine and defuzzification processes. The navigation task is divided into obstacle avoidance and target reaching tasks. Consequently, two individual FIS controllers are required to drive the actuators of the UGV, one is to avoid obstacles and the other is to reach a target. Both FIS controllers are combined through a switching mechanism to select the obstacle avoidance FIS controller if there is an obstacle, otherwise choosing reaching target FIS. The simulation results have confirmed the effectiveness of the proposed design in terms of obtaining optimal paths with shortest elapsed time.
Similarly, a new method is proposed based on an adaptive neurofuzzy inference system (ANFIS) to guide the UGV in unstructured environments. This method combines the advantages of adaptive leaning and inference fuzzy system. The simulation results have demonstrated adequate achievements in terms of obtaining shortest and feasible paths whilst avoiding static obstructing obstacles and hence reaching the specified targets speedily.
Finally, a UGV is constructed to investigate the overall performance of the proposed FIS controllers practically. The architecture of the UGV consists of three ultrasonic sensors, a magnetic compass and two quadratic decoders that they are interfaced with an Arduino microcontroller to read the sensory information. The Arduino, who acts as a slave microcontroller is serially connected with a master Raspberry Pi microcontroller. Raspberry Pi and Arduino communicate with each other based on a proposed hierarchical algorithm. Three case studies are introduced to demonstrate the effectiveness and the validation of the proposed FIS controllers and the UGV’s platform in real-time
Advanced Mobile Robotics: Volume 3
Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective
Recent Advances in Multi Robot Systems
To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems
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